Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review
Bibliographic record
Abstract
In cold regions, the high occurrence of ice jams results in severe flooding and significant damage caused by a rapid rise in water levels upstream of ice jams. These floods can be critical hydrological and hydraulic events and be a major concern for citizens, authorities, insurance companies and government agencies. In the past twenty years, several studies have been conducted in ice jam modelling and forecasting, and it has been found that predicting ice jam formation and breakup is challenging, due to the complexity of the interactions between the hydroclimatic variables leading to these processes. At this time, several mathematical models have been developed to predict breakup processes. The current methods of breakup prediction are highly empirical and site-specific. The information on the progress of the methods and the variables used to predict the occurrence, severity, and timing of the breakup ice jams still remains limited. This study summarizes the different processes contributing to ice jam formation and breakup, the various existing ice jam prediction models, and their potential and limitations regarding the improvement in ice jam predictions. An overview of the application of artificial neural networks and fuzzy logic systems in ice-related problems is presented. Genetic programming is also explained as a possible mean for ice-related problems. Although genetic programming shows promising results in hydrological modelling , it has not yet been used in ice-related problems. The review of literature highlights that data-driven and machine learning techniques provide promising means in predicting ice jams with better confidence, but more scientific research is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".